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Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6551 - 6578 Titre : Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model Type de document : Article/Communication Auteurs : Santanu Dinda, Auteur ; Nilanjana Das Chatterjee, Auteur ; Subrata Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 6551 - 6578 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] chaîne de Markov [Termes IGN] changement d'occupation du sol [Termes IGN] croissance urbaine [Termes IGN] densité du bâti [Termes IGN] espace vert [Termes IGN] Inde [Termes IGN] logique floue [Termes IGN] modèle de simulation [Termes IGN] processus de hiérarchisation analytique [Termes IGN] réseau neuronal artificiel [Termes IGN] vulnérabilité [Termes IGN] zone urbaine dense Résumé : (auteur) Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response’ framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expands LULC change analysis and provide a new dimension for UGS vulnerability assessment. Numéro de notice : A2022-726 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1952315 Date de publication en ligne : 16/07/2021 En ligne : https://doi.org/10.1080/10106049.2021.1952315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101672 [article]

Application of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2009-2034 Titre : Application of a graph convolutional network with visual and semantic features to classify urban scenes Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Shuai Jin, Auteur ; Zhanlong Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2009-2034 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] matrice de co-occurrence [Termes IGN] OpenStreetMap [Termes IGN] Pékin (Chine) [Termes IGN] point d'intérêt [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] scène urbaine Résumé : (auteur) Urban scenes consist of visual and semantic features and exhibit spatial relationships among land-use types (e.g. industrial areas are far away from the residential zones). This study applied a graph convolutional network with neighborhood information (henceforth, named the neighbour supporting graph convolutional neural network), to learn spatial relationships for urban scene classification. Furthermore, a co-occurrence analysis with visual and semantic features proceeded to improve the accuracy of urban scene classification. We tested the proposed method with the fifth ring road of Beijing with an overall classification accuracy of 0.827 and a Kappa coefficient of 0.769. In comparison with other methods, such as support vector machine, random forest, and general graph convolutional network, the case study showed that the proposed method improved about 10% in urban scene classification. Numéro de notice : A2022-740 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2048834 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2048834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101717 [article]

Estimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 1905 - 1930 Titre : Estimating urban functional distributions with semantics preserved POI embedding Type de document : Article/Communication Auteurs : Weiming Huang, Auteur ; Lizhen Cui, Auteur ; Meng Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1905 - 1930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attention (apprentissage automatique) [Termes IGN] Chine [Termes IGN] classe sémantique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] distribution spatiale [Termes IGN] échantillonnage [Termes IGN] lissage de données [Termes IGN] matrice de co-occurrence [Termes IGN] Perceptron multicouche [Termes IGN] point d'intérêt [Termes IGN] triangulation de Delaunay [Termes IGN] zone urbaine Résumé : (auteur) We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions. Numéro de notice : A2022-732 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2040510 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2040510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101714 [article]

Estimation of ionospheric total electron content using GNSS observations derived from a smartphone / Li Xu in GPS solutions, vol 26 n° 4 (October 2022)  

Public [article]  inGPS solutions > vol 26 n° 4 (October 2022) . - n° 138 Titre : Estimation of ionospheric total electron content using GNSS observations derived from a smartphone Type de document : Article/Communication Auteurs : Li Xu, Auteur ; Jiuping Zha, Auteur ; Min Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] modèle ionosphérique [Termes IGN] phase [Termes IGN] pondération [Termes IGN] série de Fourier [Termes IGN] téléphone intelligent [Termes IGN] teneur totale en électrons [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) The global navigation satellite system (GNSS) measurements to determine ionospheric total electron content (TEC) are mainly derived from expensive geodetic-grade receivers, which are not conducive to high-density placement. In this work, we present an analysis of the performance of ionospheric TEC determined by GNSS dual-frequency measurements derived from the smartphone, taking the Xiaomi 8 (XMI8) as an example. First, the ionospheric observable is retrieved from the code and carrier phase data using the carrier-to-code leveling technique and a new carrier-to-noise weighting strategy instead of an elevation weighting strategy, considering the characteristic of the GNSS measurements from smartphones. Then, the absolute ionospheric slant TEC (STEC) values are isolated from the ionospheric observables by modeling with the generalized trigonometric series function. The experimental data, covering over 120 h, were taken from two situations: one is the data collected by the original smartphone antenna; the other is the external geodetic-grade antenna. The TEC data obtained from the collocated geodetic-grade receiver are used as reference data to evaluate the performance of the TEC values from XMI8. Compared to the reference data, the evaluation results show that the ionospheric STEC extraction accuracy can reach total electron content unit (TECU) values of 0.17 and 0.11 under the two different situations in the continuous carrier phase satellite arc without cycle slips. In addition, the VTEC modeling accuracy is above 5 and 2 TECU in the two different situations, respectively. Thus, we concluded that consumer-level GNSS chipsets are highly potential in the future to increase the ionospheric monitoring station density due to their low costs and good data quality. Numéro de notice : A2022-713 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01329-w Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1007/s10291-022-01329-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101591 [article]

Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2060 - 2085 Titre : Identify urban building functions with multisource data: a case study in Guangzhou, China Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Renrong Chen, Auteur ; Yang Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2060 - 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] approche hiérarchique [Termes IGN] batiment commercial [Termes IGN] bâtiment industriel [Termes IGN] bâtiment public [Termes IGN] Canton (Kouangtoung) [Termes IGN] données multisources [Termes IGN] empreinte [Termes IGN] exploration de données [Termes IGN] Extreme Gradient Machine [Termes IGN] figure géométrique [Termes IGN] image Gaofen [Termes IGN] logement [Termes IGN] point d'intérêt [Termes IGN] zone urbaine Résumé : (auteur) Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. Numéro de notice : A2022-739 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2046756 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2046756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101716 [article]

PPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna / Xingxing Li in Journal of geodesy, vol 96 n° 10 (October 2022)  

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Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (October 2022)  

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Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)  

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Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])  

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Ambiguity resolution for smartphone GNSS precise positioning: effect factors and performance / Bofeng Li in Journal of geodesy, vol 96 n° 9 (September 2022)  

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Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)  

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Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)  

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Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)  

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A map matching-based method for electric vehicle charging station placement at directional road segment level / Zhoulin Yu in Sustainable Cities and Society, vol 84 (September 2022)  

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Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)  

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